CN117520785A - Industrial intelligent decision support system and method based on big data analysis - Google Patents
Industrial intelligent decision support system and method based on big data analysis Download PDFInfo
- Publication number
- CN117520785A CN117520785A CN202311770033.XA CN202311770033A CN117520785A CN 117520785 A CN117520785 A CN 117520785A CN 202311770033 A CN202311770033 A CN 202311770033A CN 117520785 A CN117520785 A CN 117520785A
- Authority
- CN
- China
- Prior art keywords
- industrial
- equipment
- industrial equipment
- characteristic
- data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 38
- 238000007405 data analysis Methods 0.000 title claims abstract description 16
- 238000004519 manufacturing process Methods 0.000 claims abstract description 118
- 238000009776 industrial production Methods 0.000 claims abstract description 68
- 238000012797 qualification Methods 0.000 claims description 45
- 230000008859 change Effects 0.000 claims description 37
- 238000012545 processing Methods 0.000 claims description 12
- 230000008569 process Effects 0.000 claims description 11
- 238000012423 maintenance Methods 0.000 claims description 9
- 238000012544 monitoring process Methods 0.000 claims description 7
- 230000009471 action Effects 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000003111 delayed effect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/22—Indexing; Data structures therefor; Storage structures
- G06F16/2219—Large Object storage; Management thereof
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0637—Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/04—Manufacturing
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Human Resources & Organizations (AREA)
- Physics & Mathematics (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- General Engineering & Computer Science (AREA)
- Tourism & Hospitality (AREA)
- Evolutionary Computation (AREA)
- Evolutionary Biology (AREA)
- Educational Administration (AREA)
- Bioinformatics & Computational Biology (AREA)
- General Business, Economics & Management (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Entrepreneurship & Innovation (AREA)
- Life Sciences & Earth Sciences (AREA)
- Artificial Intelligence (AREA)
- Marketing (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Game Theory and Decision Science (AREA)
- Development Economics (AREA)
- Manufacturing & Machinery (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Software Systems (AREA)
- Databases & Information Systems (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention relates to the technical field of industrial intelligent decision making, in particular to an industrial intelligent decision supporting system and method based on big data analysis, comprising the steps of constructing an industrial equipment supervision cloud platform, acquiring historical equipment state records of all industrial equipment in an industrial park, analyzing equipment running states of the industrial equipment, and analyzing production states of all the industrial equipment by combining with characteristic equipment production state data to obtain target industrial equipment; acquiring production data of an industrial production line of the target industrial equipment in the current period, and analyzing production states of all industrial equipment in the industrial production line of the target industrial equipment to obtain production scheduling data of the industrial production line of the target industrial equipment; and carrying out intelligent decision on the industrial production line in the industrial park based on the production scheduling data of the industrial production line to which the target industrial equipment belongs in the current period, and adjusting the industrial production line in the industrial park.
Description
Technical Field
The invention relates to the technical field of industrial intelligent decision making, in particular to an industrial intelligent decision supporting system and method based on big data analysis.
Background
The intelligent industrial decision-making means that in the industrial production and manufacturing process, the intelligent decision-making process is realized by utilizing the technologies of artificial intelligence, big data analysis, internet of things and the like and through analysis and processing of data, the intelligent industrial decision-making can help enterprises to improve production efficiency and reduce configuration of production optimization resources, intelligent production and manufacturing are realized, problems can be found in time through real-time monitoring and analysis of equipment states, production data and supply chain information, trends of industrial production and manufacturing are predicted, and enterprises can make more accurate and scientific decisions.
In industrial production, the working program of a single industrial device is basically set in advance by a user in a programming unit of the industrial device, which causes that problems may occur in some industrial devices in the industrial production process, but the industrial production cannot be adjusted in time, so that not only the production efficiency is delayed, but also the safety problem of the industrial production may occur.
Disclosure of Invention
The invention aims to provide an industrial intelligent decision support system and method based on big data analysis, which are used for solving the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: an industrial intelligent decision support method based on big data analysis, the method comprises:
step S100: constructing an industrial equipment supervision cloud platform, acquiring historical equipment state records of all industrial equipment in an industrial park, and analyzing equipment operation states of the industrial equipment to obtain characteristic equipment production state data of the industrial equipment;
step S200: monitoring the equipment states of all industrial equipment in the industrial park in the current period, and analyzing the production states of all industrial equipment by combining with the production state data of the characteristic equipment to obtain target industrial equipment;
step S300: acquiring production data of an industrial production line of the target industrial equipment in the current period, and analyzing production states of all industrial equipment in the industrial production line of the target industrial equipment to obtain production scheduling data of the industrial production line of the target industrial equipment;
step S400: and carrying out intelligent decision on the industrial production line in the industrial park based on the production scheduling data of the industrial production line to which the target industrial equipment belongs in the current period, and adjusting the industrial production line in the industrial park.
Further, step S100 includes:
step S101: extracting the work piece qualification rate of each work piece of the industrial equipment for processing the industrial product in each history period from the history equipment state record, and calculating a first change proportion value of the work piece qualification rate of the industrial equipment in each history period, wherein the first change proportion value P of the work piece qualification rate of the industrial equipment in the a-th history period a ：
Wherein W is a Work piece qualification rate of industrial equipment in the a-th history period; w (W) a-1 Work piece qualification rate of industrial equipment in a-1 historical period;
step S102: setting workpiece qualification rate thresholds of all industrial equipment, setting a first change proportion value threshold of the workpiece qualification rate, acquiring the workpiece qualification rate of the industrial equipment in each history period, and recording the (b) th history period as the first history period of the industrial equipment when the workpiece qualification rate of the industrial equipment in the (b) th history period is smaller than the corresponding workpiece qualification rate threshold and the first change proportion value of the workpiece qualification rate of the industrial equipment is larger than the first change proportion value threshold of the workpiece qualification rate;
step S103: setting a unit time length t Δ Acquiring each unit period in the first history period, and acquiring a time point t at which the first history period starts start Wherein the R-th unit period T in the first history period R ＝[t start +(R-1)×t Δ ,t start +R×t Δ ]；
Step S104: acquiring all equipment data of the industrial equipment acquired in each unit time period of the first history period, and calculating a second change proportion value of all equipment data of the industrial equipment in each unit time period of the first history period, wherein in the c-th unit time period of the first history period, the second change proportion value of the d-th equipment data of the industrial equipmentE c,d A value corresponding to the d-th item of equipment data of the industrial equipment in the c-th unit period of the first history period; e (E) c-1,d A value corresponding to the d-th item of equipment data of the industrial equipment in the c-1 th unit period of the first historical period;
step S105: obtaining the maximum value of the second change proportion value of the equipment data in each unit period in the first history period of the industrial equipment, obtaining the unit period of the maximum value in the first history period, and recording the value of the equipment data in the unit period as the characteristic value of the equipment data;
step S106: acquiring equipment models of all industrial equipment in an industrial park, acquiring all equipment data of the industrial equipment corresponding to the same equipment model, and selecting the minimum value of the characteristic values from the equipment data to serve as the marking characteristic value of all the equipment data;
step S107: the method comprises the steps of obtaining and collecting marking characteristic values of various equipment data of industrial equipment of various equipment models to obtain characteristic equipment production state data of the industrial equipment;
in the steps, the workpiece qualification rate is smaller than the corresponding workpiece qualification rate threshold value to indicate that the workpiece is unqualified, so that the problem occurs in the workpiece production process of the industrial product, at the moment, the workpiece qualification rate change state is required to be analyzed, a history period with larger workpiece qualification rate change, namely the first history period, is obtained, the equipment data change state of the industrial equipment in the first history period is analyzed, different numerical values of various equipment data in the industrial equipment can be obtained, and the influence degree on the industrial equipment is improved, so that the analysis on the equipment state of the industrial equipment is more accurate.
Further, step S200 includes:
step S201: collecting various equipment data of various industrial equipment in each industrial production line in the industrial park in the current period, and acquiring equipment types of various industrial equipment in each industrial production line in the industrial park in the current period;
step S202: and when the corresponding numerical value of one item of equipment data exists in the current period of the industrial equipment, marking the industrial equipment as target industrial equipment, wherein the marking characteristic numerical value of the corresponding item of equipment data in the characteristic equipment production state data of the industrial equipment with the same equipment model is larger than the marking characteristic numerical value of the corresponding item of equipment data in the characteristic equipment production state data of the industrial equipment with the same equipment model.
Further, step S300 includes:
step S301: selecting industrial equipment which is in the same step with the target industrial equipment in the production of various workpieces of the industrial product from various industrial equipment in the industrial park, and marking the industrial equipment as second industrial equipment;
step S302: obtaining the maximum value of the workpiece processing capacity of the second industrial equipment in each unit period of the history period, obtaining the equipment model of the target industrial equipment and the average value required by each maintenanceCalculating a first maintenance duration of the target industrial plant +.>Wherein, the block is when the preset industrial equipment is first overhauledLong influencing factor, ≡>0；
Step S303: when the processed workpiece of the industrial equipment in the industrial product production process is the workpiece required to be processed in the target industrial equipment production, marking the industrial equipment as the characteristic industrial equipment, acquiring the work piece workload Q of the characteristic industrial equipment in unit time, and calculating the total number of the workpieces transported by the characteristic industrial equipment to the second industrial equipmentWherein t is Δ Is a unit duration;
step S304: acquiring the workpiece processing amount actually distributed by the second industrial equipment in unit time length, and calculating the total number of the second industrial equipmentWherein X is max For maximum work piece throughput per unit length of time for the second industrial equipment,
X standard workpiece throughput actually allocated per unit time for the second industrial equipment;
step S305: acquiring the equipment distance between each second industrial equipment and the characteristic industrial equipment, and selecting H second industrial equipment closest to the characteristic industrial equipment from each second industrial equipment, wherein the H second industrial equipment is marked as the characteristic second industrial equipment;
step S306: calculating the number of feature workpieces transported by the feature industrial equipment for each feature second industrial equipment
Step S307: the method comprises the steps of collecting the number of characteristic workpieces transported by each second industrial equipment of the characteristic industrial equipment to obtain production scheduling data of an industrial production line to which target industrial equipment belongs;
the target industrial equipment in the steps is the industrial equipment with abnormal equipment data, and the industrial equipment needs to be overhauled, so that in order to avoid production problems caused by overhauling the target industrial equipment, the industrial equipment capable of replacing the target industrial equipment to process workpieces, namely the second industrial equipment, is needed to be found, the acquired characteristic industrial equipment of the target industrial equipment, the quantity of the characteristic workpieces transported by the characteristic industrial equipment and the second industrial equipment is obtained, the quantity of the workpieces transported specifically is supported by the actual data, and the industrial product production management is more scientific.
Further, step S400 includes:
step S401: powering off target industrial equipment in the industrial park in the current period, dispatching workers to overhaul the target industrial equipment, and acquiring production scheduling data of an industrial production line to which each target industrial equipment in the industrial park in the current period belongs;
step S402: and (3) acquiring the number of the characteristic workpieces of each second industrial equipment, dispatching a transport robot from the characteristic industrial equipment to transport the workpieces with the number of the characteristic workpieces to the position of the characteristic second industrial equipment, after the second industrial equipment finishes processing the workpieces transported by the characteristic workpiece equipment, dispatching the transport robot to the marking industrial equipment corresponding to the target industrial equipment by the characteristic second industrial equipment, and transporting the workpieces with the number of the marked workpieces.
In order to better realize the method, an industrial intelligent decision support system is also provided, and the intelligent decision support system comprises a production state data module, a target industrial equipment module, a production scheduling data module and an intelligent decision module;
the production state data module is used for analyzing the equipment running state of the industrial equipment to obtain the characteristic equipment production state data of the industrial equipment;
the target industrial equipment module is used for monitoring the equipment states of all industrial equipment in the industrial park in the current period, and analyzing the production states of all the industrial equipment by combining the production state data of the characteristic equipment to obtain target industrial equipment;
the production scheduling data module is used for analyzing the production states of all industrial equipment in the industrial production line to which the target industrial equipment belongs to obtain production scheduling data of the industrial production line to which the target industrial equipment belongs;
and the intelligent decision-making module is used for making intelligent decisions on the industrial production line in the industrial park and adjusting the industrial production line in the industrial park.
Further, the production state data module comprises a first change proportion value unit and a production state data unit;
the first change proportion value unit is used for calculating a first change proportion value of the work piece qualification rate of the industrial equipment in each history period;
and the production state data unit is used for acquiring the marking characteristic values of the equipment data of the industrial equipment of each equipment model, recording and collecting the marking characteristic values to obtain the production state data of the corresponding characteristic equipment of the industrial equipment of each different equipment model.
Further, the target industrial equipment module comprises a data acquisition unit and a target industrial equipment unit;
the data acquisition unit is used for acquiring the data of each piece of equipment of each piece of industrial equipment in each industrial production line in the industrial park in the current period;
and the target industrial equipment unit is used for acquiring equipment models of all industrial equipment in all industrial production lines in the industrial park in the current period, and analyzing the industrial equipment to obtain the target industrial equipment.
Further, the production scheduling data module comprises a characteristic second industrial equipment unit and a production scheduling data unit;
the characteristic second industrial equipment unit is used for acquiring equipment distances between the second industrial equipment and the characteristic industrial equipment, and selecting second industrial equipment closest to the characteristic industrial equipment from the second industrial equipment, and marking the second industrial equipment as characteristic second industrial equipment;
and the production scheduling data unit is used for collecting the quantity of the characteristic workpieces transported by each second industrial equipment of the characteristic industrial equipment to obtain production scheduling data of an industrial production line to which the target industrial equipment belongs.
Further, the intelligent decision module comprises an intelligent decision unit;
the intelligent decision unit is used for intelligently deciding an industrial production line in the industrial park, and the specific decision process comprises the steps of acquiring the number of characteristic workpieces of each second industrial device, dispatching a transport robot from the characteristic industrial devices to transport the workpieces with the number of the characteristic workpieces to the position of the characteristic second industrial devices, after the second industrial devices process the workpieces transported by the characteristic workpiece devices, the characteristic second industrial devices send the transport robot to the marking industrial devices corresponding to the target industrial devices, and transporting the workpieces with the number of the marked workpieces.
Compared with the prior art, the invention has the following beneficial effects: the invention realizes the intelligent judgment of the equipment state of the industrial equipment for producing each workpiece of the industrial product and the production state of the industrial product, and based on the historical data of the industrial equipment, the production state of the industrial equipment can be pre-judged in advance, thereby avoiding the occurrence of a large number of unqualified phenomena of the workpieces produced by the industrial equipment caused by the incapability of timely finding the production problem of the industrial equipment, and the intelligent scheduling of the production line of the industrial product can be performed in advance.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a flow chart of a method of the industrial intelligent decision support system and method based on big data analysis of the present invention;
FIG. 2 is a schematic diagram of a system and method for intelligent decision support of industrial products based on big data analysis.
Detailed Description
The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1-2, the present invention provides the following technical solutions: an industrial intelligent decision support method based on big data analysis, the method comprises:
step S100: constructing an industrial equipment supervision cloud platform, acquiring historical equipment state records of all industrial equipment in an industrial park, and analyzing equipment operation states of the industrial equipment to obtain characteristic equipment production state data of the industrial equipment;
wherein, step S100 includes:
step S101: extracting the work piece qualification rate of each work piece of the industrial equipment for processing the industrial product in each history period from the history equipment state record, and calculating a first change proportion value of the work piece qualification rate of the industrial equipment in each history period, wherein the first change proportion value P of the work piece qualification rate of the industrial equipment in the a-th history period a ：
Wherein W is a Work piece qualification rate of industrial equipment in the a-th history period; w (W) a-1 Work piece qualification rate of industrial equipment in a-1 historical period;
for example, work piece yield W of industrial equipment in history period 3 3 80% of the work piece qualification rate W of the industrial equipment in the 2 nd history period 2 Calculating a first change proportion of the work piece qualification rate of the industrial equipment in a 3 rd historical period to be 40 percent
Step S102: setting workpiece qualification rate thresholds of all industrial equipment, setting a first change proportion value threshold of the workpiece qualification rate, acquiring the workpiece qualification rate of the industrial equipment in each history period, and recording the (b) th history period as the first history period of the industrial equipment when the workpiece qualification rate of the industrial equipment in the (b) th history period is smaller than the corresponding workpiece qualification rate threshold and the first change proportion value of the workpiece qualification rate of the industrial equipment is larger than the first change proportion value threshold of the workpiece qualification rate;
step S103: setting a unit time length t Δ Acquiring each unit period in the first history period, and acquiring a time point t at which the first history period starts start Wherein the R-th unit period T in the first history period R ＝[t start +(R-1)×t Δ ,t start +R×t Δ ]；
Step S104: acquiring all equipment data of the industrial equipment acquired in each unit time period of the first history period, and calculating a second change proportion value of all equipment data of the industrial equipment in each unit time period of the first history period, wherein in the c-th unit time period of the first history period, the second change proportion value of the d-th equipment data of the industrial equipmentE c,d A value corresponding to the d-th item of equipment data of the industrial equipment in the c-th unit period of the first history period; e (E) c-1,d A value corresponding to the d-th item of equipment data of the industrial equipment in the c-1 th unit period of the first historical period;
step S105: obtaining the maximum value of the second change proportion value of the equipment data in each unit period in the first history period of the industrial equipment, obtaining the unit period of the maximum value in the first history period, and recording the value of the equipment data in the unit period as the characteristic value of the equipment data;
for example, each item of equipment data includes voltage data, current data, and the like of the industrial equipment;
step S106: acquiring equipment models of all industrial equipment in an industrial park, acquiring all equipment data of the industrial equipment corresponding to the same equipment model, and selecting the minimum value of the characteristic values from the equipment data to serve as the marking characteristic value of all the equipment data;
step S107: the method comprises the steps of obtaining and collecting marking characteristic values of various equipment data of industrial equipment of various equipment models to obtain characteristic equipment production state data of the industrial equipment;
step S200: monitoring the equipment states of all industrial equipment in the industrial park in the current period, and analyzing the production states of all industrial equipment by combining with the production state data of the characteristic equipment to obtain target industrial equipment;
wherein, step S200 includes:
step S201: collecting various equipment data of various industrial equipment in each industrial production line in the industrial park in the current period, and acquiring equipment types of various industrial equipment in each industrial production line in the industrial park in the current period;
step S202: when the corresponding numerical value of a certain item of equipment data exists in the current period of the industrial equipment, marking the industrial equipment as target industrial equipment, wherein the marking characteristic numerical value of the corresponding item of equipment data in the characteristic equipment production state data of the industrial equipment with the same equipment model is larger than the marking characteristic numerical value of the corresponding item of equipment data in the characteristic equipment production state data of the industrial equipment with the same equipment model;
step S300: acquiring production data of an industrial production line of the target industrial equipment in the current period, and analyzing production states of all industrial equipment in the industrial production line of the target industrial equipment to obtain production scheduling data of the industrial production line of the target industrial equipment;
for example, production data of an industrial production line to which the target industrial equipment belongs, including a distance between each industrial equipment on the industrial production line, an amount of workpieces processed by each industrial equipment in a unit time length, and the like;
wherein, step S300 includes:
step S301: selecting industrial equipment which is in the same step with the target industrial equipment in the production of various workpieces of the industrial product from various industrial equipment in the industrial park, and marking the industrial equipment as second industrial equipment;
step S302: obtaining the maximum value of the workpiece processing amount of the second industrial equipment in each unit time period of the history period, and obtaining the target industrial equipmentBelonging to the equipment model and the average value required by each maintenanceCalculating a first maintenance duration of the target industrial plant +.>Wherein, oc is the influence factor of the first maintenance duration of the preset industrial equipment, oc>0；
Step S303: when the processed workpiece of the industrial equipment in the industrial product production process is the workpiece required to be processed in the target industrial equipment production, marking the industrial equipment as the characteristic industrial equipment, acquiring the work piece workload Q of the characteristic industrial equipment in unit time, and calculating the total number of the workpieces transported by the characteristic industrial equipment to the second industrial equipmentWherein t is Δ Is a unit duration;
for example, a time period t corresponding to the unit time period Δ For 2min, the first maintenance time length W of the target industrial equipment is 30min, the work piece work quantity Q of the characteristic industrial equipment in unit time length is 1000, and the total number of the work pieces transported by the characteristic industrial equipment to the second industrial equipment is calculated
Step S304: acquiring the workpiece processing amount actually distributed by the second industrial equipment in unit time length, and calculating the total number of the second industrial equipmentWherein X is max For maximum work piece throughput per unit length of time for the second industrial equipment,
X standard workpiece throughput actually allocated per unit time for the second industrial equipment;
step S305: acquiring the equipment distance between each second industrial equipment and the characteristic industrial equipment, and selecting H second industrial equipment closest to the characteristic industrial equipment from each second industrial equipment, wherein the H second industrial equipment is marked as the characteristic second industrial equipment;
step S306: calculating the number of feature workpieces transported by the feature industrial equipment for each feature second industrial equipment
Step S307: the method comprises the steps of collecting the number of characteristic workpieces transported by each second industrial equipment of the characteristic industrial equipment to obtain production scheduling data of an industrial production line to which target industrial equipment belongs;
step S400: based on production scheduling data of an industrial production line to which target industrial equipment belongs in a current period, intelligent decision is made on the industrial production line in the industrial park, and the industrial production line in the industrial park is adjusted;
wherein, step S400 includes:
step S401: powering off target industrial equipment in the industrial park in the current period, dispatching workers to overhaul the target industrial equipment, and acquiring production scheduling data of an industrial production line to which each target industrial equipment in the industrial park in the current period belongs;
step S402: the method comprises the steps of obtaining the number of characteristic workpieces of each second industrial device, dispatching a transport robot to transport the workpieces with the number of characteristic workpieces to the position of the characteristic second industrial devices from the characteristic industrial devices, after the second industrial devices process the workpieces transported by the characteristic workpiece devices, the characteristic second industrial devices send the transport robot to the marking industrial devices corresponding to the target industrial devices, and transport the workpieces with the number of marked workpieces;
in order to better realize the method, an industrial intelligent decision support system is also provided, and the intelligent decision support system comprises a production state data module, a target industrial equipment module, a production scheduling data module and an intelligent decision module;
the production state data module is used for analyzing the equipment running state of the industrial equipment to obtain the characteristic equipment production state data of the industrial equipment;
the target industrial equipment module is used for monitoring the equipment states of all industrial equipment in the industrial park in the current period, and analyzing the production states of all the industrial equipment by combining the production state data of the characteristic equipment to obtain target industrial equipment;
the production scheduling data module is used for analyzing the production states of all industrial equipment in the industrial production line to which the target industrial equipment belongs to obtain production scheduling data of the industrial production line to which the target industrial equipment belongs;
the intelligent decision-making module is used for making intelligent decisions on the industrial production lines in the industrial park and adjusting the industrial production lines in the industrial park;
the production state data module comprises a first change proportion value unit and a production state data unit;
the first change proportion value unit is used for calculating a first change proportion value of the work piece qualification rate of the industrial equipment in each history period;
the production state data unit is used for acquiring the marking characteristic values of the equipment data of the industrial equipment of each equipment model, recording and collecting the marking characteristic values to obtain the production state data of the corresponding characteristic equipment of the industrial equipment of each different equipment model;
the target industrial equipment module comprises a data acquisition unit and a target industrial equipment unit;
the data acquisition unit is used for acquiring the data of each piece of equipment of each piece of industrial equipment in each industrial production line in the industrial park in the current period;
the target industrial equipment unit is used for acquiring equipment models of all industrial equipment in all industrial production lines in the industrial park in the current period, and analyzing the industrial equipment to obtain target industrial equipment;
the production scheduling data module comprises a characteristic second industrial equipment unit and a production scheduling data unit;
the characteristic second industrial equipment unit is used for acquiring equipment distances between the second industrial equipment and the characteristic industrial equipment, and selecting second industrial equipment closest to the characteristic industrial equipment from the second industrial equipment, and marking the second industrial equipment as characteristic second industrial equipment;
the production scheduling data unit is used for collecting the number of the characteristic workpieces transported by each second industrial equipment of the characteristic industrial equipment to obtain production scheduling data of an industrial production line to which the target industrial equipment belongs;
the intelligent decision module comprises an intelligent decision unit;
the intelligent decision unit is used for intelligently deciding an industrial production line in the industrial park, and the specific decision process comprises the steps of acquiring the number of characteristic workpieces of each second industrial device, dispatching a transport robot from the characteristic industrial devices to transport the workpieces with the number of the characteristic workpieces to the position of the characteristic second industrial devices, after the second industrial devices process the workpieces transported by the characteristic workpiece devices, the characteristic second industrial devices send the transport robot to the marking industrial devices corresponding to the target industrial devices, and transporting the workpieces with the number of the marked workpieces.
It should be noted that in this document relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. An industrial intelligent decision support method based on big data analysis is characterized by comprising the following steps:
step S100: constructing an industrial equipment supervision cloud platform, acquiring historical equipment state records of all industrial equipment in an industrial park, and analyzing equipment operation states of the industrial equipment to obtain characteristic equipment production state data of the industrial equipment;
step S200: monitoring the equipment states of all industrial equipment in the industrial park in the current period, and analyzing the production states of all industrial equipment by combining with the production state data of the characteristic equipment to obtain target industrial equipment;
step S300: acquiring production data of an industrial production line of the target industrial equipment in the current period, and analyzing production states of all industrial equipment in the industrial production line of the target industrial equipment to obtain production scheduling data of the industrial production line of the target industrial equipment;
step S400: and carrying out intelligent decision on the industrial production line in the industrial park based on the production scheduling data of the industrial production line to which the target industrial equipment belongs in the current period, and adjusting the industrial production line in the industrial park.
2. The industrial intelligent decision support method based on big data analysis of claim 1, wherein the step S100 comprises:
step S101: extracting the work piece qualification rate of each work piece of the industrial equipment for processing the industrial product in each history period from the history equipment state record, and calculating a first change proportion value of the work piece qualification rate of the industrial equipment in each history period, wherein the first change proportion value P of the work piece qualification rate of the industrial equipment in the a-th history period a ：
Wherein W is a Work piece qualification rate of industrial equipment in the a-th history period; w (W) a-1 Work piece qualification rate of industrial equipment in a-1 historical period;
step S102: setting workpiece qualification rate thresholds of all industrial equipment, setting a first change proportion value threshold of the workpiece qualification rate, acquiring the workpiece qualification rate of the industrial equipment in each history period, and recording the (b) th history period as the first history period of the industrial equipment when the workpiece qualification rate of the industrial equipment in the (b) th history period is smaller than the corresponding workpiece qualification rate threshold and the first change proportion value of the workpiece qualification rate of the industrial equipment is larger than the first change proportion value threshold of the workpiece qualification rate;
step S103: setting a unit time length t Δ Acquiring each unit period in the first history period, and acquiring a time point t at which the first history period starts start Wherein the R-th unit period T in the first history period R ＝[t start +(R-1)×t Δ ,t start +R×t Δ ]；
Step S104: acquiring all equipment data of the industrial equipment acquired in each unit time period of the first history period, and calculating a second change proportion value of all equipment data of the industrial equipment in each unit time period of the first history period, wherein in the c-th unit time period of the first history period, the second change proportion value of the d-th equipment data of the industrial equipmentE c,d A value corresponding to the d-th item of equipment data of the industrial equipment in the c-th unit period of the first history period; e (E) c-1,d A value corresponding to the d-th item of equipment data of the industrial equipment in the c-1 th unit period of the first historical period;
step S105: obtaining the maximum value of the second change proportion value of the equipment data in each unit period in the first history period of the industrial equipment, obtaining the unit period of the maximum value in the first history period, and recording the value of the equipment data in the unit period as the characteristic value of the equipment data;
step S106: acquiring equipment models of all industrial equipment in an industrial park, acquiring all equipment data of the industrial equipment corresponding to the same equipment model, and selecting the minimum value of the characteristic values from the equipment data to serve as the marking characteristic value of all the equipment data;
step S107: and obtaining and collecting the marked characteristic values of the equipment data of the industrial equipment of each equipment model to obtain the production state data of the characteristic equipment of the industrial equipment.
3. The industrial intelligent decision support method based on big data analysis of claim 2, wherein the step S200 comprises:
step S201: collecting various equipment data of various industrial equipment in each industrial production line in the industrial park in the current period, and acquiring equipment types of various industrial equipment in each industrial production line in the industrial park in the current period;
step S202: and when the corresponding numerical value of one item of equipment data exists in the current period of the industrial equipment, marking the industrial equipment as target industrial equipment, wherein the marking characteristic numerical value of the corresponding item of equipment data in the characteristic equipment production state data of the industrial equipment with the same equipment model is larger than the marking characteristic numerical value of the corresponding item of equipment data in the characteristic equipment production state data of the industrial equipment with the same equipment model.
4. The industrial intelligent decision support method based on big data analysis according to claim 3, wherein the step S300 comprises:
step S301: selecting industrial equipment which is in the same step with the target industrial equipment in the production of various workpieces of the industrial product from various industrial equipment in the industrial park, and marking the industrial equipment as second industrial equipment;
step S302: obtaining the maximum value of the workpiece processing capacity of the second industrial equipment in each unit period of the history period, and obtaining the equipment to which the target industrial equipment belongsModel and average value required for each maintenanceCalculating a first maintenance duration of the target industrial plant +.>Wherein, oc is the influence factor of the first maintenance duration of the preset industrial equipment, oc>0；
Step S303: when the processed workpiece of the industrial equipment in the industrial product production process is the workpiece required to be processed in the target industrial equipment production, marking the industrial equipment as the characteristic industrial equipment, acquiring the work piece workload Q of the characteristic industrial equipment in unit time, and calculating the total number of the workpieces transported by the characteristic industrial equipment to the second industrial equipmentWherein t is Δ Is a unit duration;
step S304: acquiring the workpiece processing amount actually distributed by the second industrial equipment in unit time length, and calculating the total number of the second industrial equipmentWherein X is max X is the maximum work piece throughput of the second industrial equipment in unit time standard Workpiece throughput actually allocated per unit time for the second industrial equipment;
step S305: acquiring the equipment distance between each second industrial equipment and the characteristic industrial equipment, and selecting H second industrial equipment closest to the characteristic industrial equipment from each second industrial equipment, wherein the H second industrial equipment is marked as the characteristic second industrial equipment;
step S306: calculating the number of feature workpieces transported by the feature industrial equipment for each feature second industrial equipment
Step S307: and collecting the number of the characteristic workpieces transported by each second industrial equipment of the characteristic industrial equipment to obtain production scheduling data of an industrial production line to which the target industrial equipment belongs.
5. The industrial intelligent decision support method based on big data analysis of claim 4, wherein the step S400 comprises:
step S401: powering off target industrial equipment in the industrial park in the current period, dispatching workers to overhaul the target industrial equipment, and acquiring production scheduling data of an industrial production line to which each target industrial equipment in the industrial park in the current period belongs;
step S402: and (3) acquiring the number of the characteristic workpieces of each second industrial equipment, dispatching a transport robot from the characteristic industrial equipment to transport the workpieces with the number of the characteristic workpieces to the position of the characteristic second industrial equipment, after the second industrial equipment finishes processing the workpieces transported by the characteristic workpiece equipment, dispatching the transport robot to the marking industrial equipment corresponding to the target industrial equipment by the characteristic second industrial equipment, and transporting the workpieces with the number of the marked workpieces.
6. An industrial intelligent decision support system applied to the industrial intelligent decision support method based on big data analysis as claimed in any one of claims 1-5, wherein the intelligent decision support system comprises a production state data module, a target industrial equipment module, a production scheduling data module and an intelligent decision module;
the production state data module is used for analyzing the equipment running state of the industrial equipment to obtain the characteristic equipment production state data of the industrial equipment;
the target industrial equipment module is used for monitoring the equipment states of all industrial equipment in the industrial park in the current period, and analyzing the production states of all the industrial equipment by combining with the production state data of the characteristic equipment to obtain target industrial equipment;
the production scheduling data module is used for analyzing the production states of all industrial equipment in the industrial production line to which the target industrial equipment belongs to obtain production scheduling data of the industrial production line to which the target industrial equipment belongs;
the intelligent decision module is used for intelligently deciding the industrial production line in the industrial park and adjusting the industrial production line in the industrial park.
7. The industrial intelligent decision support system according to claim 6, wherein the production status data module comprises a first change ratio value unit, a production status data unit;
the first change proportion value unit is used for calculating a first change proportion value of the work piece qualification rate of the industrial equipment in each history period;
the production state data unit is used for acquiring the marking characteristic values of the equipment data of the industrial equipment of each equipment model, and recording and gathering the marking characteristic values to obtain the production state data of the corresponding characteristic equipment of the industrial equipment of each different equipment model.
8. The industrial intelligent decision support system according to claim 6, wherein the target industrial equipment module comprises a data acquisition unit and a target industrial equipment unit;
the data acquisition unit is used for acquiring the data of each piece of equipment of each piece of industrial equipment in each industrial production line in the industrial park in the current period;
and the target industrial equipment unit is used for acquiring equipment models of all industrial equipment in all industrial production lines in the industrial park in the current period, and analyzing the industrial equipment to obtain the target industrial equipment.
9. The industrial intelligent decision support system of claim 6, wherein the production scheduling data module comprises a characteristic second industrial equipment unit, a production scheduling data unit;
the characteristic second industrial equipment unit is used for acquiring equipment distances between the second industrial equipment and the characteristic industrial equipment, and selecting second industrial equipment closest to the characteristic industrial equipment from the second industrial equipment, and marking the second industrial equipment as characteristic second industrial equipment;
the production scheduling data unit is used for collecting the quantity of the characteristic workpieces transported by each second industrial equipment of the characteristic industrial equipment to obtain production scheduling data of an industrial production line to which the target industrial equipment belongs.
10. The industrial intelligent decision support system of claim 6, wherein the intelligent decision module comprises an intelligent decision unit;
the intelligent decision unit is used for intelligently deciding an industrial production line in an industrial park, and the specific decision process comprises the steps of acquiring the number of characteristic workpieces of each second industrial device, dispatching a conveying robot from the characteristic industrial devices to convey the workpieces with the number of the characteristic workpieces to the second industrial devices, after the second industrial devices process the workpieces conveyed by the characteristic workpiece devices, conveying the second industrial devices to the marking industrial devices corresponding to the target industrial devices, and conveying the workpieces with the number of the marking workpieces.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311770033.XA CN117520785A (en) | 2023-12-21 | 2023-12-21 | Industrial intelligent decision support system and method based on big data analysis |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311770033.XA CN117520785A (en) | 2023-12-21 | 2023-12-21 | Industrial intelligent decision support system and method based on big data analysis |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117520785A true CN117520785A (en) | 2024-02-06 |
Family
ID=89751453
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311770033.XA Pending CN117520785A (en) | 2023-12-21 | 2023-12-21 | Industrial intelligent decision support system and method based on big data analysis |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117520785A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117911415A (en) * | 2024-03-20 | 2024-04-19 | 珠海市捷锐科技有限公司 | Automatic equipment supervision system and method based on machine vision |
-
2023
- 2023-12-21 CN CN202311770033.XA patent/CN117520785A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117911415A (en) * | 2024-03-20 | 2024-04-19 | 珠海市捷锐科技有限公司 | Automatic equipment supervision system and method based on machine vision |
| CN117911415B (en) * | 2024-03-20 | 2024-05-24 | 珠海市捷锐科技有限公司 | Automatic equipment supervision system and method based on machine vision |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN117520785A (en) | Industrial intelligent decision support system and method based on big data analysis | |
| CN105607566B (en) | A kind of self-adapting regulation method of production material | |
| CN101364108B (en) | Dynamic dispatching method for workshop working based on matter drive in complex large system environment | |
| CN112309000B (en) | Machine tool utilization rate monitoring system of cloud platform | |
| CN111538295A (en) | Flexible production management system | |
| CN111752241B (en) | Product process modeling method for intelligent factory | |
| Sowmya et al. | A review on effective utilization of resources using overall equipment effectiveness by reducing six big losses | |
| CN116011759A (en) | Intelligent workshop management system based on Internet of things | |
| CN113608482A (en) | Intelligent monitoring method, system and management system for precision machining tool | |
| CN107831738A (en) | The cutter and production planning management system of bearing mnanufacture | |
| CN109597382A (en) | Coal machine and manufactures intelligence blanking production system | |
| CN110371633A (en) | A kind of automatic Material Handling System | |
| CN110675073A (en) | Method and device for realizing real-time updating processing of scheduled production in production process | |
| CN116050788A (en) | Industrial intelligent scheduling management system | |
| JP2009157871A (en) | Production management method | |
| Toth et al. | Elaborating Industry 4.0 compatible DSS for enhancing production system effectiveness | |
| CN114117135A (en) | Big data information analysis method and system based on deep learning and back measurement algorithm | |
| CN113844813A (en) | Slab identification, tracking, scheduling and management system based on machine vision | |
| CN113554405A (en) | Network collaborative manufacturing system for supply chain of automobile industry | |
| CN111176174A (en) | Multifunctional supervision system for stamping workshop | |
| CN111985848A (en) | Heuristic algorithm-based bearing end cover production scheduling and scheduling optimization method | |
| CN117635000B (en) | LMS production logistics scheduling method based on industrial Internet | |
| Zhang | Research on Information Platform of Plate Cutting and Groove Workshop | |
| CN114137921B (en) | Real-time allocation system and allocation method for intelligent production workshop of sanitary equipment | |
| CN118195252A (en) | Part machining planning system and method based on multitasking self-adaptive coordination |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |